题    目：Augmenting the unreturned for field data with information on returned failures only（基于已有故障信息的未知外场数据填充方案）

演 讲 人：叶志盛，新加坡国立大学助理教授

主 持 人：翟庆庆，十大正规网投官网平台(中国)有限公司讲师

时    间：2018年10月19日（周五），下午16:00

地    点：校本部东区十大正规网投官网平台实477室

主办单位：十大正规网投官网平台(中国)有限公司、十大正规网投官网平台(中国)有限公司青年教师联谊会

演讲人简介：

    叶志盛博士本科与博士分别就读于清华大学与新加坡国立大学，现为新加坡国立大学工业系统工程与管理系助理教授。

    叶博士主要研究方向为工业统计、退化数据分析、寿命与复发数据分析、可靠性建模等，在相关领域已发表高水平论文60余篇，包括Technometrics, IIE Transactions, Journal of Quality Technology等。

演讲内容简介：

    Field data are an important source of reliability information for many commercial products. Because field data are often collected by the maintenance department, information on failed and returned units is well maintained. Nevertheless, information on unreturned units is generally unavailable. The unavailability leads to truncation in the lifetime data. This study proposes a data augmentation algorithm for this type of truncated field return data with returned failures available only. The algorithm is based on an idea to reveal the hidden unobserved lifetimes. Theoretical justifications of the procedure for augmenting the hidden unobserved are given. On the other hand, the algorithm is iterative in nature. Asymptotic properties of the estimators from the iterations are investigated. Both point estimation and the information matrix of the parameters can be directly obtained from the algorithm. In addition, a by-product of the algorithm is a non-parametric estimator of the installation time distribution. An example from an asset-rich company is given to demonstrate the proposed methods.

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